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Full text of "NASA Technical Reports Server (NTRS) 19860004331: Crop Characteristics Research: Growth and Reflectance Analysis"

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N86- 13800 


CROP CHARACTERISTIC RESEARCH: G. D. Badhwar 

GROWTH AND REFLECTANCE ANALYSIS Johnson Space Center 

Houston, TX 77058 


Justification, Goals, and Objectives 

Much of the early research in remote sensing follows along developing 
"spectral signatures" of cover types. It was, however, found that a 
signature from an unknown cover class could not be matched to a catalog 
value of known cover class. This approach was abandoned and "supervised 
classification" schemes followed. These were not efficient and required 
extensive training. It has been patently clear that data acquired at a 
single time could not separate cover types. 

A large portion of the proposed research has concentrated on modeling the 
temporal behavior of agricultural crops and on removing the need for any 
training data in remote sensing surveys — the key to which is the solution 
of the so-called "signature extension" problem. 

A clear need to develop spectral estimaters of crop ontogenic stages and 
yield has existed even though various correlations have been developed. 
Considerable effort in developing techniques to estimate these variables 
was devoted to this work. 

The need to accurately evaluate existing canopy reflectance model (s), 
improve these models, use them to understand the "crop signatures," and 
estimate leaf area index was the third objective of the proposed work. 

The next section gives a synposis of this research effort. 


Technical Approach 


The technical approach consisted of first developing an accurate model that 
would describe the temporal development of various spectral transforms with 
time, henceforth called a profile, that would depend primarily on crop 
characteristics. It would thus permit features to be extracted from real 
spectral data that describes a specific crop and would not depend on external 
variables such as row direction, sun zenith angle, the atmospheric state, 
etc. Having extracted a very small set of features use the canopy reflectance 
model (s) to gain (a) better understanding and limitations of existing canopy 
models, (b) use the model (s) to accure a deeper physical understanding of 
why these features permit crop separation and develop method(s) of deciding, 
a priori, what features would permit crop separation, and (c) explorate the 
applicability of these models to natural forest communities. 


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Results 


The current research effort has shewn that the Kauth-Thomas (K-T) greenness, 
in the spectral space of both the multispectral spectral scanner or the 
thematic mapper, can be described by a model of the form, 

o/2 

/ 2ee \ a ? 
p(t) = P 0 + (p m - P 0 ) ( — ) (t - t Q ) Exp [-3(t - t Q ) 2 ] (1) 

where p(t) the K-T greenness as a function of time, p m , the maximum value of 
greenness reached at time of peak greenness 


t p - 



) 


p 0 the value of soil greenness at times at and before emergence, t 0 , and a 
and 3 are two crop and condition specifics constant. These two constant are 
related to the inflection points of the profile 


ti - t 0 = 


(2a + 1) - v/8a + 1 

4 e 


1/2 


t 2 - t 0 = 


"(2a + 1) +/8a + 1 


1/2 


4(5 


This research effort has established that the peak greenness above the soil 
line, G max = Pm “ Pq> the separation 


1/2 



1 a f 

1 “1 

cr = 1 2 “ 1 1 = 

— + — [1 

- (1 - - )l 

2(5 2(5 L 

a “* 


1/2 


and the time of peak greenness, t p ,'are three characteristics or features 
that carry 95% of all information (Fisher information criteria) available 
in both spectral and temporal data and has led to a drastic reduction in 
the number of variables and a simplification of classifier design. 


Figure 1 shows the power of these features in separating two summer crops, 
corn/soybeans, based on data extracted from the thematic mapper. The axis 
out of the plane of paper is the number of pixels, the other two axes beifK) 
G max and 0 days. The distributions are more or less gaussian and provide 
excellent separabi 1 i ty . Not only has it been shown that these features (G max , 
o, and tp) are applicable to different sensor systems but are truly 
"signature extendable" over vast areas in the United States and, for the 
first time ever in remote sensing, to areas in Argentina. In particular, 
it has been found that, (i) G max (soybeans) > G max (corn), (ii) o (soybeans) < 
a (corn), and (iii) t p (soybeans) > t p (corn). 


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jsrifW-n*?; s tmzvf 

Illo" e f his fill' COr ?; and s °? bea,ls and appears to be true for 

,n c S TSI^ f Tc r ^o„. 


It has also been shown that integral, 

*2 

f p(t)dt, 
fc l 

which acts in a manner very similar to the leaf area duration i<? ct-renniw 
inThl^Ir . to Jf lel J 1n the case of b oth corn and soybeans. More research* 
autom^nv 1r AnH 10 K- 1S f^ al1ed f ° r; however » the Potential of a true integrated 

an5°S^„rlre1f«r„mf„ P 9 r | a d |p“ ,0n SySt “ ipp,i “ b,e tp S'° bal “rn ’ 

In order to understand the reasoning behind why (soybeans! > a 
aI«ost ooavorsely, the existing canopy reflecting Sodl°f llle ose^Villll’ 

llflll nX^e'l™ Z]‘ I? £”* 

le r |a n |a f bm“ d ca 0 n Pt |riaulla7ld?a n “''i?!.' 0 Pr ° ,tde * <* ' 

The 'exf sting canopy reflectance models'had not been subiected fn a 

.. ’s , d CUPID canopy reflectance models were very carefully evaluated for 
e 0 ‘° rn and soybeans. AH of these models captured the s!lllnt 
l!fSn 0 r he Canopy reflec tance, with the SAIL model showing the best 
overall performance. However, it was found that it is necessary to include 
u e U !l , r , ° n ? 1 characteristics of leaves into these Sde?s 

iMlIJinn'if ? *5 perf ? rmance of the SAIL model before and afte- the 
inclusion °f leaf specular reflectance systematic angular defic encies of 

of th^Io^s! 0 ^^^;^^ KTJfSj;nyIi h «i s S^5T v SuS e .r ?!Si n :s. 

— Trr :l r °‘ ' 

£ II r ilaqir^oll-: lllrilrfl^cl-ln'^l^irj??! d “ 

?rE i; 

(1). The meaning of the t] , t p , and a would of course be verj - different. 


'J/ 


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r O 


Significance 

(1) For the first time in remote sensing, crop features have been found 
that are truly "signature extendable." In case of corn and soybeans 
they have been shown to be applicable to vast geographic regions of 
the United States for four years 1978, 1979, 1980, and 1982 and are 
extendable to Argentina. Based on SAIL canopy reflectance model, the 
reasons for this applicability have been understood. 

(2) It has been shown that critical ontogenetic crop stages can be estimated 
from spectral data. Based on this work and more detailed work, it is 
suggested that it may be more accurate to estimate crop phenology 
using spectral data than current methods. 

(3) Preliminary evidence suggests that the area of greenness profile from ' 
t] (when new leaf development stops) to t2 (dent) is strongly correlated 
to yield. 

The above three results have made it possible to seriously consider an 
automatic and objective crop production system. 

(4) An improved canopy reflectance model has been developed that includes 
the leaf specular component. 

(5) The effectiveness of these models in estimation of leaf area index 
of wheat, corn, and soybeans and recently in study of forest species 
separation and aspen leaf area estimation has been demonstrated. 

Future 


Most of the current work on feature extraction, ontogenetic stage and yield 
has been done on corn and soybean. Some start was made on wheat, barley, 
and oats. The work on spring grains should be intensified. Currently, 
no technique exists to separate the three crops. Additional work on 
ontogenetic stage and yield of corn and soybeans still needs to be done. 

The performance of existing canopy models on forest canopies has been found 
to be sadly lacking, much more so for coniferous forest than for decidous 
forest. Major improvements in these models are called for and a corresponding 
adequate input data set must be collected. Techniques to estimate leaf 
area index or phytomass of vegetation cannot be developed realiably without 
such an effort. 


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Figure 1 


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RELATIVE AZIMUTH 
(DEGREES) 


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FIGURE 3